Conference paper
On Data and Parameter Estimation Using the Variational Bayesian EM-algorithm for Block-fading Frequency-selective MIMO Channels
A general Variational Bayesian framework for iterative data and parameter estimation for coherent detection is introduced as a generalization of the EM-algorithm. Explicit solutions are given for MIMO channel estimation with Gaussian prior and noise covariance estimation with inverse-Wishart prior. Simulation of a GSM-like system provides empirical proof that the VBEM-algorithm is able to provide better performance than the EM-algorithm.
However, if the posterior distribution is highly peaked, the VBEM-algorithm approaches the EM-algorithm and the gain disappears. The potential gain is therefore greatest in systems with a small amount of observations compared to the number of parameters to be estimated.
Language: | English |
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Publisher: | IEEE |
Year: | 2006 |
Pages: | IV-465-IV-468 |
Proceedings: | 2006 IEEE International Conference on Acoustics, Speech and Signal Processing |
ISBN: | 142440469X , 142440469x , 1509091653 , 9781424404698 and 9781509091652 |
ISSN: | 2379190x and 15206149 |
Types: | Conference paper |
DOI: | 10.1109/ICASSP.2006.1661006 |
ORCIDs: | Larsen, Jan |
Bayes methods Bayesian methods Channel estimation Frequency estimation GSM-like system Gaussian noise Informatics MIMO MIMO channel estimation MIMO systems Maximum likelihood estimation Parameter estimation Transmitters Vectors block-fading frequency-selective MIMO channels channel estimation coherent detection demodulation expectation-maximisation algorithm fading channels iterative data estimation multiple input multiple output channels noise covariance estimation parameter estimation variational Bayesian EM-algorithm